



"""simple docstring"""







import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


UpperCamelCase     =							logging.getLogger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[Any]							  =     "summarization"
			A__	:					Dict							  =     ["loss"]
			A__	:					Optional[Any]							  =     ROUGE_KEYS
			A__	:					Optional[int]							  =     "rouge2"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				if hparams.sortish_sampler and hparams.gpus > 1:
					A__											=					False
				elif hparams.max_tokens_per_batch is not None:
					if hparams.gpus > 1:
						raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training"		)
					if hparams.sortish_sampler:
						raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"		)

				super().__init__(SCREAMING_SNAKE_CASE__						,	num_labels=SCREAMING_SNAKE_CASE__						,	mode=self.mode						,	**SCREAMING_SNAKE_CASE__		)
				use_task_specific_params(self.model						,	"summarization"		)
				save_git_info(self.hparams.output_dir		)
				A__											=					Path(self.output_dir		) / "metrics.json"
				A__											=					Path(self.output_dir		) / "hparams.pkl"
				pickle_save(self.hparams						,	self.hparams_save_path		)
				A__											=					0
				A__											=					defaultdict(SCREAMING_SNAKE_CASE__		)
				A__											=					self.config.model_type
				A__											=					self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

				A__											=					{
				    "data_dir": self.hparams.data_dir,
				    "max_source_length": self.hparams.max_source_length,
				    "prefix": self.model.config.prefix or "",
				}
				A__											=					{
				    "train": self.hparams.n_train,
				    "val": self.hparams.n_val,
				    "test": self.hparams.n_test,
				}
				A__											=					{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

				A__											=					{
				    "train": self.hparams.max_target_length,
				    "val": self.hparams.val_max_target_length,
				    "test": self.hparams.test_max_target_length,
				}
				assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
				assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
				if self.hparams.freeze_embeds:
					freeze_embeds(self.model		)
				if self.hparams.freeze_encoder:
					freeze_params(self.model.get_encoder()		)
					assert_all_frozen(self.model.get_encoder()		)

				A__											=					get_git_info()["repo_sha"]
				A__											=					hparams.num_workers
				A__											=					None  # default to config
				if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,	SCREAMING_SNAKE_CASE__		):
					A__											=					self.tokenizer.lang_code_to_id[hparams.tgt_lang]
					A__											=					self.decoder_start_token_id
				A__											=					(
				    SeqaSeqDataset if hasattr(self.tokenizer						,	"prepare_seq2seq_batch"		) else LegacySeqaSeqDataset
				)
				A__											=					False
				A__											=					self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
				if self.hparams.eval_max_gen_length is not None:
					A__											=					self.hparams.eval_max_gen_length
				else:
					A__											=					self.model.config.max_length
				A__											=					self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Dict[str, List[str]]:
				A__											=					{
				    k: self.tokenizer.batch_decode(v.tolist()		) if "mask" not in k else v.shape for k, v in batch.items()
				}
				save_json(SCREAMING_SNAKE_CASE__						,	Path(self.output_dir		) / "text_batch.json"		)
				save_json({k: v.tolist() for k, v in batch.items()}						,	Path(self.output_dir		) / "tok_batch.json"		)

				A__											=					True
				return readable_batch


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				return self.model(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					self.tokenizer.batch_decode(
				    SCREAMING_SNAKE_CASE__						,	skip_special_tokens=SCREAMING_SNAKE_CASE__						,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__		)
				return lmap(str.strip						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				A__											=					self.tokenizer.pad_token_id
				A__							,							A__											=					batch["input_ids"], batch["attention_mask"]
				A__											=					batch["labels"]
				if isinstance(self.model						,	SCREAMING_SNAKE_CASE__		):
					A__											=					self.model._shift_right(SCREAMING_SNAKE_CASE__		)
				else:
					A__											=					shift_tokens_right(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
					A__											=					decoder_input_ids
					self.save_readable_batch(SCREAMING_SNAKE_CASE__		)

				A__											=					self(SCREAMING_SNAKE_CASE__						,	attention_mask=SCREAMING_SNAKE_CASE__						,	decoder_input_ids=SCREAMING_SNAKE_CASE__						,	use_cache=SCREAMING_SNAKE_CASE__		)
				A__											=					outputs["logits"]
				if self.hparams.label_smoothing == 0:
					# Same behavior as modeling_bart.py, besides ignoring pad_token_id
					A__											=					nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE__		)

					assert lm_logits.shape[-1] == self.vocab_size
					A__											=					ce_loss_fct(lm_logits.view(-1						,	lm_logits.shape[-1]		)						,	tgt_ids.view(-1		)		)
				else:
					A__											=					nn.functional.log_softmax(SCREAMING_SNAKE_CASE__						,	dim=-1		)
					A__							,							A__											=					label_smoothed_nll_loss(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	self.hparams.label_smoothing						,	ignore_index=SCREAMING_SNAKE_CASE__		)
				return (loss,)


			@property
			def   snake_case__	(      self		)      ->	int:
				return self.tokenizer.pad_token_id


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					self._step(SCREAMING_SNAKE_CASE__		)

				A__											=					dict(zip(self.loss_names						,	SCREAMING_SNAKE_CASE__		)		)
				# tokens per batch
				A__											=					batch["input_ids"].ne(self.pad		).sum() + batch["labels"].ne(self.pad		).sum()
				A__											=					batch["input_ids"].shape[0]
				A__											=					batch["input_ids"].eq(self.pad		).sum()
				A__											=					batch["input_ids"].eq(self.pad		).float().mean()
				# TODO(SS): make a wandb summary metric for this
				return {"loss": loss_tensors[0], "log": logs}


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return self._generative_step(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__="val"		)      ->	Dict:
				self.step_count += 1
				A__											=					{k: torch.stack([x[k] for x in outputs]		).mean() for k in self.loss_names}
				A__											=					losses["loss"]
				A__											=					{
				    k: np.array([x[k] for x in outputs]		).mean() for k in self.metric_names + ["gen_time", "gen_len"]
				}
				A__											=					(
				    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
				)
				A__											=					torch.tensor(SCREAMING_SNAKE_CASE__		).type_as(SCREAMING_SNAKE_CASE__		)
				generative_metrics.update({k: v.item() for k, v in losses.items()}		)
				losses.update(SCREAMING_SNAKE_CASE__		)
				A__											=					{f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
				A__											=					self.step_count
				self.metrics[prefix].append(SCREAMING_SNAKE_CASE__		)  # callback writes this to self.metrics_save_path
				A__											=					flatten_list([x["preds"] for x in outputs]		)
				return {
				    "log": all_metrics,
				    "preds": preds,
				    f"""{prefix}_loss""": loss,
				    f"""{prefix}_{self.val_metric}""": metric_tensor,
				}


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return calculate_rouge(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	dict:
				A__											=					time.time()

				# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
				A__											=					self.model.generate(
				    batch["input_ids"]						,	attention_mask=batch["attention_mask"]						,	use_cache=SCREAMING_SNAKE_CASE__						,	decoder_start_token_id=self.decoder_start_token_id						,	num_beams=self.eval_beams						,	max_length=self.eval_max_length						,	)
				A__											=					(time.time() - ta) / batch["input_ids"].shape[0]
				A__											=					self.ids_to_clean_text(SCREAMING_SNAKE_CASE__		)
				A__											=					self.ids_to_clean_text(batch["labels"]		)
				A__											=					self._step(SCREAMING_SNAKE_CASE__		)
				A__											=					dict(zip(self.loss_names						,	SCREAMING_SNAKE_CASE__		)		)
				A__											=					self.calc_generative_metrics(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				A__											=					np.mean(lmap(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)		)
				base_metrics.update(gen_time=SCREAMING_SNAKE_CASE__						,	gen_len=SCREAMING_SNAKE_CASE__						,	preds=SCREAMING_SNAKE_CASE__						,	target=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				return base_metrics


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				return self._generative_step(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				return self.validation_epoch_end(SCREAMING_SNAKE_CASE__						,	prefix="test"		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	SeqaSeqDataset:
				A__											=					self.n_obs[type_path]
				A__											=					self.target_lens[type_path]
				A__											=					self.dataset_class(
				    self.tokenizer						,	type_path=SCREAMING_SNAKE_CASE__						,	n_obs=SCREAMING_SNAKE_CASE__						,	max_target_length=SCREAMING_SNAKE_CASE__						,	**self.dataset_kwargs						,	)
				return dataset


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False		)      ->	DataLoader:
				A__											=					self.get_dataset(SCREAMING_SNAKE_CASE__		)

				if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
					A__											=					dataset.make_sortish_sampler(SCREAMING_SNAKE_CASE__						,	distributed=self.hparams.gpus > 1		)
					return DataLoader(
					    SCREAMING_SNAKE_CASE__						,	batch_size=SCREAMING_SNAKE_CASE__						,	collate_fn=dataset.collate_fn						,	shuffle=SCREAMING_SNAKE_CASE__						,	num_workers=self.num_workers						,	sampler=SCREAMING_SNAKE_CASE__						,	)

				elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
					A__											=					dataset.make_dynamic_sampler(
					    self.hparams.max_tokens_per_batch						,	distributed=self.hparams.gpus > 1		)
					return DataLoader(
					    SCREAMING_SNAKE_CASE__						,	batch_sampler=SCREAMING_SNAKE_CASE__						,	collate_fn=dataset.collate_fn						,	num_workers=self.num_workers						,	)
				else:
					return DataLoader(
					    SCREAMING_SNAKE_CASE__						,	batch_size=SCREAMING_SNAKE_CASE__						,	collate_fn=dataset.collate_fn						,	shuffle=SCREAMING_SNAKE_CASE__						,	num_workers=self.num_workers						,	sampler=SCREAMING_SNAKE_CASE__						,	)


			def   snake_case__	(      self		)      ->	DataLoader:
				A__											=					self.get_dataloader("train"						,	batch_size=self.hparams.train_batch_size						,	shuffle=SCREAMING_SNAKE_CASE__		)
				return dataloader


			def   snake_case__	(      self		)      ->	DataLoader:
				return self.get_dataloader("val"						,	batch_size=self.hparams.eval_batch_size		)


			def   snake_case__	(      self		)      ->	DataLoader:
				return self.get_dataloader("test"						,	batch_size=self.hparams.eval_batch_size		)







			@staticmethod
			def   snake_case__	(      SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				add_generic_args(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				parser.add_argument(
				    "--max_source_length"						,	default=1024						,	type=SCREAMING_SNAKE_CASE__						,	help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )						,	)
				parser.add_argument(
				    "--max_target_length"						,	default=56						,	type=SCREAMING_SNAKE_CASE__						,	help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )						,	)
				parser.add_argument(
				    "--val_max_target_length"						,	default=142						,	type=SCREAMING_SNAKE_CASE__						,	help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )						,	)
				parser.add_argument(
				    "--test_max_target_length"						,	default=142						,	type=SCREAMING_SNAKE_CASE__						,	help=(
				        "The maximum total input sequence length after tokenization. Sequences longer "
				        "than this will be truncated, sequences shorter will be padded."
				    )						,	)
				parser.add_argument("--freeze_encoder"						,	action="store_true"		)
				parser.add_argument("--freeze_embeds"						,	action="store_true"		)
				parser.add_argument("--sortish_sampler"						,	action="store_true"						,	default=SCREAMING_SNAKE_CASE__		)
				parser.add_argument("--overwrite_output_dir"						,	action="store_true"						,	default=SCREAMING_SNAKE_CASE__		)
				parser.add_argument("--max_tokens_per_batch"						,	type=SCREAMING_SNAKE_CASE__						,	default=SCREAMING_SNAKE_CASE__		)
				parser.add_argument("--logger_name"						,	type=SCREAMING_SNAKE_CASE__						,	choices=["default", "wandb", "wandb_shared"]						,	default="default"		)
				parser.add_argument("--n_train"						,	type=SCREAMING_SNAKE_CASE__						,	default=-1						,	required=SCREAMING_SNAKE_CASE__						,	help="# examples. -1 means use all."		)
				parser.add_argument("--n_val"						,	type=SCREAMING_SNAKE_CASE__						,	default=500						,	required=SCREAMING_SNAKE_CASE__						,	help="# examples. -1 means use all."		)
				parser.add_argument("--n_test"						,	type=SCREAMING_SNAKE_CASE__						,	default=-1						,	required=SCREAMING_SNAKE_CASE__						,	help="# examples. -1 means use all."		)
				parser.add_argument(
				    "--task"						,	type=SCREAMING_SNAKE_CASE__						,	default="summarization"						,	required=SCREAMING_SNAKE_CASE__						,	help="# examples. -1 means use all."		)
				parser.add_argument("--label_smoothing"						,	type=SCREAMING_SNAKE_CASE__						,	default=0.0						,	required=SCREAMING_SNAKE_CASE__		)
				parser.add_argument("--src_lang"						,	type=SCREAMING_SNAKE_CASE__						,	default=""						,	required=SCREAMING_SNAKE_CASE__		)
				parser.add_argument("--tgt_lang"						,	type=SCREAMING_SNAKE_CASE__						,	default=""						,	required=SCREAMING_SNAKE_CASE__		)
				parser.add_argument("--eval_beams"						,	type=SCREAMING_SNAKE_CASE__						,	default=SCREAMING_SNAKE_CASE__						,	required=SCREAMING_SNAKE_CASE__		)
				parser.add_argument(
				    "--val_metric"						,	type=SCREAMING_SNAKE_CASE__						,	default=SCREAMING_SNAKE_CASE__						,	required=SCREAMING_SNAKE_CASE__						,	choices=["bleu", "rouge2", "loss", None]		)
				parser.add_argument("--eval_max_gen_length"						,	type=SCREAMING_SNAKE_CASE__						,	default=SCREAMING_SNAKE_CASE__						,	help="never generate more than n tokens"		)
				parser.add_argument("--save_top_k"						,	type=SCREAMING_SNAKE_CASE__						,	default=1						,	required=SCREAMING_SNAKE_CASE__						,	help="How many checkpoints to save"		)
				parser.add_argument(
				    "--early_stopping_patience"						,	type=SCREAMING_SNAKE_CASE__						,	default=-1						,	required=SCREAMING_SNAKE_CASE__						,	help=(
				        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
				        " val_check_interval will effect it."
				    )						,	)
				return parser






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     "translation"
			A__	:					List[Any]							  =     ["loss"]
			A__	:					Optional[Any]							  =     ["bleu"]
			A__	:					Optional[Any]							  =     "bleu"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				super().__init__(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				A__											=					hparams.src_lang
				A__											=					hparams.tgt_lang







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	dict:
				return calculate_bleu(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)



def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Optional[Any]=None     )	-> SummarizationModule:






	"""simple docstring"""





	Path(args.output_dir     ).mkdir(exist_ok=UpperCAmelCase_     )
	check_output_dir(UpperCAmelCase_,	expected_items=3     )

	if model is None:
		if "summarization" in args.task:
			A__											=					SummarizationModule(UpperCAmelCase_     )
		else:
			A__											=					TranslationModule(UpperCAmelCase_     )
	A__											=					Path(args.data_dir     ).name
	if (
	    args.logger_name == "default"
	    or args.fast_dev_run
	    or str(args.output_dir     ).startswith("/tmp"     )
	    or str(args.output_dir     ).startswith("/var"     )
	):
		A__											=					True  # don't pollute wandb logs unnecessarily
	elif args.logger_name == "wandb":
		from pytorch_lightning.loggers import WandbLogger

		A__											=					os.environ.get("WANDB_PROJECT",	UpperCAmelCase_     )
		A__											=					WandbLogger(name=model.output_dir.name,	project=UpperCAmelCase_     )

	elif args.logger_name == "wandb_shared":
		from pytorch_lightning.loggers import WandbLogger

		A__											=					WandbLogger(name=model.output_dir.name,	project=F"""hf_{dataset}"""     )

	if args.early_stopping_patience >= 0:
		A__											=					get_early_stopping_callback(model.val_metric,	args.early_stopping_patience     )
	else:
		A__											=					False

	A__											=					args.val_metric == "loss"
	A__											=					generic_train(
	    UpperCAmelCase_,	UpperCAmelCase_,	logging_callback=SeqaSeqLoggingCallback(),	checkpoint_callback=get_checkpoint_callback(
	        args.output_dir,	model.val_metric,	args.save_top_k,	UpperCAmelCase_     ),	early_stopping_callback=UpperCAmelCase_,	logger=UpperCAmelCase_,	)
	pickle_save(model.hparams,	model.output_dir / "hparams.pkl"     )
	if not args.do_predict:
		return model

	A__											=					""
	A__											=					sorted(glob.glob(os.path.join(args.output_dir,	"*.ckpt"     ),	recursive=UpperCAmelCase_     )     )
	if checkpoints:
		A__											=					checkpoints[-1]
		A__											=					checkpoints[-1]
	trainer.logger.log_hyperparams(model.hparams     )

	# test() without a model tests using the best checkpoint automatically
	trainer.test()
	return model


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			UpperCamelCase     =							pl.Trainer.add_argparse_args(parser)
			UpperCamelCase     =							SummarizationModule.add_model_specific_args(parser, os.getcwd())

			UpperCamelCase     =							parser.parse_args()

			main(args)






"""simple docstring"""



import math
import unittest


def 			_UpperCamelCase						(    _A   )     ->	bool:





    """simple docstring"""

    assert isinstance(_A       ,   _A   ) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5       ,   int(math.sqrt(_A   ) + 1   )       ,   6   ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True




class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							int  )		->Optional[int]:






        '''simple docstring'''






        self.assertTrue(is_prime(2  )  )
        self.assertTrue(is_prime(3  )  )
        self.assertTrue(is_prime(5  )  )
        self.assertTrue(is_prime(7  )  )
        self.assertTrue(is_prime(11  )  )
        self.assertTrue(is_prime(13  )  )
        self.assertTrue(is_prime(17  )  )
        self.assertTrue(is_prime(19  )  )
        self.assertTrue(is_prime(23  )  )
        self.assertTrue(is_prime(29  )  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase  ):
            is_prime(-19  )
        self.assertFalse(
            is_prime(0  )  ,       """Zero doesn't have any positive factors, primes must have exactly two."""  ,       )
        self.assertFalse(
            is_prime(1  )  ,       """One only has 1 positive factor, primes must have exactly two."""  ,       )
        self.assertFalse(is_prime(2 * 2  )  )
        self.assertFalse(is_prime(2 * 3  )  )
        self.assertFalse(is_prime(3 * 3  )  )
        self.assertFalse(is_prime(3 * 5  )  )
        self.assertFalse(is_prime(3 * 5 * 7  )  )


if __name__ == "__main__":
 unittest.main()